Today, we are more connected as a society than ever before. Data is continuously being mined and stored from various sources by a plethora of companies and individuals. Data may be, among others, data from any type of sensor, data tracked by companies or data relevant to the public at large. Examples of data affecting the public at large may be traffic data, weather data, stock price data, etc.
Companies often use sensors to track the condition or movement of their equipment, the state of processes and inventory conditions. This may be referred to as an data ecosystem of a company. For example, sensors are used at oil wells to monitor various statistics of machines used in the oil drilling process. Additionally, sensors are used to monitor the storage and transportation of inventory. For example, sensors may be placed at intervals along an oil pipeline to monitor the physical condition of the pipeline and enable detection of issues such as leaks in the pipeline, physical damage to the pipeline and/or other similar emergencies. Sensors may be used to track the amount of oil at any point in the pipeline, the water density in the pipeline, the rate of flow of oil at any point in the pipeline, etc. In addition, sensors may be used to track the temperature of the interior of the pipeline, the exterior of the pipeline or the humidity surrounding the pipeline.
In addition, companies track their inventory and sales at their distribution centers. For example, an oil distribution company will track the amount of oil it sells to each gas station, airport, shipping yard, etc. The company may track the price at which each barrel of oil was sold, the date of the sale, etc. The company may also track its supply chain and distribution processes such that the time and steps taken to refine the oil are known. Furthermore, the location of each transport vessel (e.g., ship or truck) will be tracked throughout the distribution process (e.g., via global positioning system).
Currently, some forms of gathered data have been used to predict future events. For example, weather data, e.g., data relevant to the public at large, is routinely collected and used to predict future weather systems in a given geographic area. For example, data may be collected from thermometers, barometers, windsocks, humidity sensors, air pressure sensors, etc.
Currently, in order to determine the reliability of a piece of equipment, failure testing is done in a lab where identical samples of the piece of equipment are tested for extended hours under possible failure conditions to determine the Mean Time to Failure (MTTF). The statistical measure of the MTTF gives a general idea of the durability of a typical piece of equipment under predefined failure conditions. A second technique is known as Mean Time Between Failure (MTBF). MTBF provides mean time measurements between possible failures. Typically original equipment manufacturers (OEMs) determine the MTTF and MTBF for their equipment.
However, even though all of this data may be collected and stored by various sources, the use of such data in predictive or preventive analytics has thus far been limited. For example, the data used to predict the weather forecast (e.g., data relevant to the public at large) has not been combined with data collected by companies regarding their oil inventory and shipments (e.g., business application data such as the enterprise resource planning (ERP) of the company) along with a leak found in an upstream oil transporting pipeline, wherein the business faces a constraint in fulfilling a demand without violating compliance regulations.